An ensemble learning model for predicting cancer-specific survival of muscle-invasive bladder cancer patients undergoing bladder preservation therapy.

Background: More muscle-invasive bladder cancer (MIBC) patients are now eligible for bladder-preserving therapy (BPT), underscoring the need for precision medicine. This study aimed to identify prognostic predictors and construct a predictive model among MIBC patients who undergo BPT. Methods: Data relating to MIBC patients were obtained from the Surveillance, Epidemiology and End Results (SEER) database from 2004 to 2016. Eleven features were included to establish multiple models. The predictive effectiveness was assessed using receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA) and clinical impact curve (CIC). SHapley Additive exPlanations (SHAP) were used to explain the impact of features on the predicted targets. Results: The ROC showed that Catboost and Random Forest (RF) obtained better predictive discrimination in both 3- and 5-year models [test set area under curves (AUC) =0.80 and 0.83, respectively]. Furthermore, Catboost showed better performance in calibration plots, DCA and CIC. SHAP analysis indicated that age, M stage, tumor size, chemotherapy, T stage and gender were the most important features in the model for predicting the 3-year cancer-specific survival (CSS). In contrast, M stage, age, tumor size and gender as well as the N and T stages were the most important features for predicting the 5-year CSS. Conclusions: The Catboost model exhibits the highest predictive performance and clinical utility, potentially aiding clinicians in making optimal individualized decisions for MIBC patients with BPT.